The recent advent of discriminative feature extraction has shown that improved recognition results can be obtained by using an integrated optimization of both the preprocessing and classification stages. Previous studies have also demonstrated that Mel-warped discrete fourier transform (DFT) features, subject to appropriate transformation in a state-dependent manner, are more effective than the conventional, model-independent speech features, called Mel-frequency cepstral coefficients (MFCCs). To further the improvements in the speech recognition field, it is desirable to use optimization of both preprocessing and classification in speech recognition methods and systems.